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--- |
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library_name: peft |
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license: gemma |
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base_model: google/codegemma-7b |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: code-bench-CodeGemma-7B-cgv1-ds_v3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# code-bench-CodeGemma-7B-cgv1-ds_v3 |
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This model is a fine-tuned version of [google/codegemma-7b](https://huggingface.co/google/codegemma-7b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0663 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.7003 | 0.0530 | 50 | 0.6702 | |
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| 0.5467 | 0.1061 | 100 | 0.5399 | |
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| 0.4662 | 0.1591 | 150 | 0.4138 | |
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| 0.3608 | 0.2121 | 200 | 0.3042 | |
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| 0.3032 | 0.2652 | 250 | 0.2450 | |
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| 0.2313 | 0.3182 | 300 | 0.2067 | |
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| 0.1953 | 0.3713 | 350 | 0.1729 | |
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| 0.1701 | 0.4243 | 400 | 0.1495 | |
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| 0.1593 | 0.4773 | 450 | 0.1382 | |
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| 0.1491 | 0.5304 | 500 | 0.1334 | |
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| 0.1668 | 0.5834 | 550 | 0.1282 | |
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| 0.1433 | 0.6364 | 600 | 0.1259 | |
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| 0.1457 | 0.6895 | 650 | 0.1241 | |
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| 0.1476 | 0.7425 | 700 | 0.1215 | |
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| 0.139 | 0.7955 | 750 | 0.1176 | |
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| 0.1209 | 0.8486 | 800 | 0.1159 | |
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| 0.1365 | 0.9016 | 850 | 0.1148 | |
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| 0.1239 | 0.9547 | 900 | 0.1157 | |
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| 0.116 | 1.0077 | 950 | 0.1097 | |
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| 0.1145 | 1.0607 | 1000 | 0.1104 | |
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| 0.1187 | 1.1146 | 1050 | 0.1067 | |
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| 0.117 | 1.1676 | 1100 | 0.1069 | |
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| 0.1219 | 1.2206 | 1150 | 0.1059 | |
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| 0.1192 | 1.2737 | 1200 | 0.1052 | |
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| 0.1296 | 1.3267 | 1250 | 0.1023 | |
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| 0.1016 | 1.3797 | 1300 | 0.1016 | |
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| 0.1051 | 1.4328 | 1350 | 0.1011 | |
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| 0.1207 | 1.4858 | 1400 | 0.1016 | |
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| 0.1132 | 1.5388 | 1450 | 0.1031 | |
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| 0.1143 | 1.5919 | 1500 | 0.0997 | |
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| 0.1089 | 1.6449 | 1550 | 0.0988 | |
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| 0.1164 | 1.6980 | 1600 | 0.0966 | |
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| 0.1092 | 1.7510 | 1650 | 0.0961 | |
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| 0.1056 | 1.8040 | 1700 | 0.0957 | |
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| 0.1072 | 1.8571 | 1750 | 0.0948 | |
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| 0.1029 | 1.9101 | 1800 | 0.0942 | |
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| 0.1117 | 1.9631 | 1850 | 0.0931 | |
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| 0.1126 | 2.0162 | 1900 | 0.0931 | |
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| 0.104 | 2.0700 | 1950 | 0.0944 | |
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| 0.1094 | 2.1230 | 2000 | 0.0925 | |
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| 0.1044 | 2.1761 | 2050 | 0.0944 | |
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| 0.0981 | 2.2291 | 2100 | 0.0926 | |
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| 0.1031 | 2.2822 | 2150 | 0.0915 | |
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| 0.0933 | 2.3352 | 2200 | 0.0919 | |
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| 0.1085 | 2.3882 | 2250 | 0.0917 | |
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| 0.1106 | 2.4413 | 2300 | 0.0905 | |
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| 0.0988 | 2.4943 | 2350 | 0.0897 | |
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| 0.0909 | 2.5473 | 2400 | 0.0883 | |
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| 0.1025 | 2.6004 | 2450 | 0.0874 | |
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| 0.1016 | 2.6534 | 2500 | 0.0873 | |
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| 0.0927 | 2.7064 | 2550 | 0.0860 | |
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| 0.0942 | 2.7595 | 2600 | 0.0854 | |
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| 0.0888 | 2.8125 | 2650 | 0.0859 | |
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| 0.091 | 2.8656 | 2700 | 0.0851 | |
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| 0.0922 | 2.9186 | 2750 | 0.0855 | |
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| 0.0949 | 2.9716 | 2800 | 0.0839 | |
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| 0.0855 | 3.0247 | 2850 | 0.0841 | |
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| 0.0955 | 3.0777 | 2900 | 0.0831 | |
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| 0.0831 | 3.1307 | 2950 | 0.0817 | |
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| 0.0843 | 3.1838 | 3000 | 0.0814 | |
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| 0.0756 | 3.2368 | 3050 | 0.0812 | |
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| 0.0893 | 3.2898 | 3100 | 0.0806 | |
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| 0.0787 | 3.3429 | 3150 | 0.0827 | |
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| 0.0842 | 3.3959 | 3200 | 0.0790 | |
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| 0.079 | 3.4490 | 3250 | 0.0791 | |
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| 0.0797 | 3.5020 | 3300 | 0.0773 | |
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| 0.0774 | 3.5550 | 3350 | 0.0777 | |
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| 0.0751 | 3.6081 | 3400 | 0.0779 | |
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| 0.079 | 3.6611 | 3450 | 0.0781 | |
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| 0.0849 | 3.7141 | 3500 | 0.0762 | |
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| 0.0852 | 3.7672 | 3550 | 0.0759 | |
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| 0.0742 | 3.8202 | 3600 | 0.0770 | |
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| 0.0719 | 3.8732 | 3650 | 0.0755 | |
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| 0.07 | 3.9263 | 3700 | 0.0757 | |
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| 0.0778 | 3.9793 | 3750 | 0.0759 | |
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| 0.0792 | 4.0324 | 3800 | 0.0751 | |
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| 0.0705 | 4.0854 | 3850 | 0.0745 | |
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| 0.0679 | 4.1384 | 3900 | 0.0741 | |
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| 0.0619 | 4.1915 | 3950 | 0.0734 | |
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| 0.0689 | 4.2445 | 4000 | 0.0731 | |
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| 0.0653 | 4.2975 | 4050 | 0.0732 | |
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| 0.0678 | 4.3506 | 4100 | 0.0733 | |
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| 0.07 | 4.4036 | 4150 | 0.0719 | |
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| 0.0656 | 4.4566 | 4200 | 0.0739 | |
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| 0.062 | 4.5097 | 4250 | 0.0732 | |
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| 0.0676 | 4.5627 | 4300 | 0.0718 | |
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| 0.0668 | 4.6158 | 4350 | 0.0722 | |
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| 0.0701 | 4.6688 | 4400 | 0.0718 | |
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| 0.067 | 4.7218 | 4450 | 0.0709 | |
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| 0.0686 | 4.7749 | 4500 | 0.0722 | |
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| 0.0649 | 4.8279 | 4550 | 0.0751 | |
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| 0.0711 | 4.8809 | 4600 | 0.0708 | |
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| 0.0747 | 4.9340 | 4650 | 0.0711 | |
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| 0.0622 | 4.9870 | 4700 | 0.0700 | |
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| 0.0634 | 5.0400 | 4750 | 0.0695 | |
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| 0.0714 | 5.0931 | 4800 | 0.0756 | |
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| 0.0615 | 5.1461 | 4850 | 0.0732 | |
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| 0.0612 | 5.1992 | 4900 | 0.0704 | |
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| 0.0599 | 5.2522 | 4950 | 0.0686 | |
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| 0.0567 | 5.3052 | 5000 | 0.0679 | |
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| 0.0593 | 5.3583 | 5050 | 0.0673 | |
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| 0.0576 | 5.4113 | 5100 | 0.0675 | |
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| 0.0628 | 5.4643 | 5150 | 0.0664 | |
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| 0.0572 | 5.5174 | 5200 | 0.0660 | |
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| 0.06 | 5.5704 | 5250 | 0.0659 | |
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| 0.0568 | 5.6234 | 5300 | 0.0660 | |
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| 0.058 | 5.6765 | 5350 | 0.0656 | |
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| 0.0559 | 5.7295 | 5400 | 0.0650 | |
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| 0.0549 | 5.7826 | 5450 | 0.0652 | |
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| 0.0605 | 5.8356 | 5500 | 0.0649 | |
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| 0.0539 | 5.8886 | 5550 | 0.0641 | |
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| 0.0567 | 5.9417 | 5600 | 0.0637 | |
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| 0.057 | 5.9947 | 5650 | 0.0654 | |
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| 0.0482 | 6.0477 | 5700 | 0.0663 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
